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Found 13 papers, 5 papers with code
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS).
A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart Dynamics
To this end, we developed a novel conditional generative model, MeshHeart, to learn the distribution of cardiac shape and motion patterns.
LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods.
Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.
Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images
In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images.
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis.
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task.
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
Learning-Based Quality Control for Cardiac MR Images
The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.
Mixed Neural Network Approach for Temporal Sleep Stage Classification
Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge.
